gaussian_sigma = float(sys.argv[4]) if argc > 4 else 1.5
result_image_path = (sys.argv[5]) if argc > 5 else 'image/fig2_result.png'
mode = (sys.argv[6]) if argc > 6 else 'constant'
noise_rate = float(sys.argv[7]) if argc > 7 else 100
kernel = mysubroutines.fspecial(kernel_size, gaussian_sigma)
f = mpimg.imread(image_path)
if len(f.shape) < 3:
f = np.reshape(f, [*f.shape, 1])
if np.max(f) > 1.0:
f = f / 255
max_step = int(sys.argv[2]) if argc > 2 else 10
u_hat = mysubroutines.add_NoiseAndBluf(f,
kernel,
lambda_weight,
mode=mode,
noise_rate=noise_rate)
h = plt.figure()
ax = h.add_subplot(111)
u1 = u_hat.copy()
if f.shape[2] == 1:
u1 = u_hat[:, :, 0]
ax.imshow(u1, cmap=plt.cm.gray)
h.savefig('image/fig2_noise.png')
u = mysubroutines.mysolver(u_hat,
kernel,
lambda_weight,
result_image_path = (sys.argv[4]) if argc > 4 else 'image/fig1_result.png'
raw_isnoise = int(sys.argv[5]) if argc > 5 else 0
kernel_size = 1
gaussian_sigma = 1.5
noise_rate = 0.1
kernel = mysubroutines.fspecial(kernel_size, gaussian_sigma)
f = mpimg.imread(image_path)
if len(f.shape) < 3:
f = np.reshape(f, [*f.shape, 1])
if np.max(f) > 1.0:
f = f / 255
if raw_isnoise == 0:
u_hat = mysubroutines.add_NoiseAndBluf(f, kernel, noise_rate=noise_rate)
else:
u_hat = f
u1 = u_hat.copy()
if f.shape[2] == 1:
u1 = u_hat[:, :, 0]
misc.imsave('image/fig1_noise.png', u1)
plt.close('all')
h1 = plt.figure()
ax1 = h1.add_subplot(121)
ax1.imshow(u1, cmap=plt.cm.gray)
u = mysubroutines.mysolver(u_hat, C=C, K=K, mode_L=mode_L, T=T)
u2 = u.copy()
if f.shape[2] == 1: